Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Mechatronics and Automation, ICMA 2020 |
| Pages | 912-919 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-7281-6416-8 |
| Publication status | Published - 2020 |
Publication series
| Name | IEEE International Conference on Mechatronics and Automation, ICMA 2020 |
|---|
Abstract
This paper presents a sensitivity-based approach for optimal model design and identification of the dynamics of a state-of-the-art industrial robot considering process-related restrictions. The possibility of parameter excitation for subsequent identification of the model parameters is severely limited due to restrictions imposed by the process environment, especially the limited available workspace. Without sufficient parameter excitation, a satisfactory quality of the full model identification cannot be achieved, since non-excited parameters cannot be identified correctly. Furthermore, optimal excitation requires time-consuming calculations and distinct experiments during which the robot is not available for daily operation. It is therefore of interest to use process-related trajectories instead of dedicated excitation trajectories, which is expected to deteriorate the identifiability of the model parameters. For this reason, the presented method uses a sensitivity-based approach allowing model order reduction in the identification process. The resulting model contains only those parameters excited by the excitation trajectory. For process-related trajectories this implies the model being limited to parameters relevant for the process. In experiments with a standard serial-link industrial robot controlled by standard industrial programmable logic control and servo inverters it is shown that the method produces significantly reduced models with a good measure of identifiability and quality.
Keywords
- Identification, Industrial Robot, Model order reduction, Sensitivity
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
Cite this
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- BibTeX
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2020 IEEE International Conference on Mechatronics and Automation, ICMA 2020. 2020. p. 912-919 9233709 (IEEE International Conference on Mechatronics and Automation, ICMA 2020).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Sensitivity-based Model Reduction for In-Process Identification of Industrial Robots Inverse Dynamics
AU - Volkmann, Björn
AU - Kaczor, Daniel
AU - Tantau, Mathias
AU - Schappler, Moritz
AU - Ortmaier, Tobias
PY - 2020
Y1 - 2020
N2 - This paper presents a sensitivity-based approach for optimal model design and identification of the dynamics of a state-of-the-art industrial robot considering process-related restrictions. The possibility of parameter excitation for subsequent identification of the model parameters is severely limited due to restrictions imposed by the process environment, especially the limited available workspace. Without sufficient parameter excitation, a satisfactory quality of the full model identification cannot be achieved, since non-excited parameters cannot be identified correctly. Furthermore, optimal excitation requires time-consuming calculations and distinct experiments during which the robot is not available for daily operation. It is therefore of interest to use process-related trajectories instead of dedicated excitation trajectories, which is expected to deteriorate the identifiability of the model parameters. For this reason, the presented method uses a sensitivity-based approach allowing model order reduction in the identification process. The resulting model contains only those parameters excited by the excitation trajectory. For process-related trajectories this implies the model being limited to parameters relevant for the process. In experiments with a standard serial-link industrial robot controlled by standard industrial programmable logic control and servo inverters it is shown that the method produces significantly reduced models with a good measure of identifiability and quality.
AB - This paper presents a sensitivity-based approach for optimal model design and identification of the dynamics of a state-of-the-art industrial robot considering process-related restrictions. The possibility of parameter excitation for subsequent identification of the model parameters is severely limited due to restrictions imposed by the process environment, especially the limited available workspace. Without sufficient parameter excitation, a satisfactory quality of the full model identification cannot be achieved, since non-excited parameters cannot be identified correctly. Furthermore, optimal excitation requires time-consuming calculations and distinct experiments during which the robot is not available for daily operation. It is therefore of interest to use process-related trajectories instead of dedicated excitation trajectories, which is expected to deteriorate the identifiability of the model parameters. For this reason, the presented method uses a sensitivity-based approach allowing model order reduction in the identification process. The resulting model contains only those parameters excited by the excitation trajectory. For process-related trajectories this implies the model being limited to parameters relevant for the process. In experiments with a standard serial-link industrial robot controlled by standard industrial programmable logic control and servo inverters it is shown that the method produces significantly reduced models with a good measure of identifiability and quality.
KW - Model order reduction
KW - Sensitivity
KW - Identification
KW - Industrial Robot
KW - Identification
KW - Industrial Robot
KW - Model order reduction
KW - Sensitivity
UR - http://www.scopus.com/inward/record.url?scp=85096597679&partnerID=8YFLogxK
U2 - 10.15488/10355
DO - 10.15488/10355
M3 - Conference contribution
SN - 978-1-7281-6417-5
T3 - IEEE International Conference on Mechatronics and Automation, ICMA 2020
SP - 912
EP - 919
BT - 2020 IEEE International Conference on Mechatronics and Automation, ICMA 2020
ER -